How to Discover Sensitive Data in Your AWS Environment

We all have those things in our careers that take us by surprise. For security teams, one of the most common is the chaos of customer data.

Take AWS – AWS offers a powerful infrastructure for data storage and management, but its flexibility makes it easy to lose oversight over the data inside. Data is often scattered across S3, RDS, Redshift, and other services. Teams constantly create and modify datasets, making it hard to keep track of sensitive information.

Chaotic data environments are stressful. They create compliance risks, can lead to breaches that erode customer trust, and make insider threats harder to detect. And when security teams don’t have clear visibility, it’s only a matter of time before something slips through the cracks.

Sensitive information can be stored across multiple AWS services, sometimes without security teams even knowing.

A continuously updated data inventory is essential – it should automatically track changes across your AWS environment. A data-centric security approach ensures you always know where sensitive data resides, who is accessing it, and whether permissions are appropriate. Without these capabilities, maintaining AWS data security becomes significantly harder.

You also need:

• Context-aware discovery: A solution that doesn’t just rely on pattern matching but understands data relationships and context to reduce false positives when identifying sensitive data.
• Cross-account and multi-region visibility: Sensitive data is often spread across multiple AWS accounts and regions. A centralized discovery tool should provide a unified view of all data assets.
• Integration with IAM and access logs: Knowing where sensitive data is isn’t enough. Security teams need visibility into who is accessing it, using AWS IAM policies and CloudTrail logs to track behavior.

Some teams try to track sensitive data manually. This might involve searching S3 buckets, writing queries against databases, or reviewing access logs.

This approach doesn’t scale. It’s time-consuming, error-prone, and can’t keep up with constant changes. By the time you complete a manual audit, the data landscape has already shifted. It also requires deep familiarity with AWS services and data structures, making it difficult for teams with high turnover or multiple data owners.

Some companies build their own tools to scan AWS for sensitive data. This often involves custom scripts, AWS Glue, or other pipeline-based methods.

It sounds like a good idea, but in reality, DIY solutions require ongoing maintenance. Data structures change. New services get added. Security teams end up spending more time maintaining their internal tools than actually securing data.

In-house solutions also struggle with scalability. Scanning large datasets in AWS requires optimized queries and efficient storage access patterns, or else costs can skyrocket. Without a dedicated engineering team, homegrown solutions often become slow, outdated, or inaccurate.

DLP tools are a common go-to for security teams, but they have major gaps. Many security engineers find that DLP struggles to keep up with modern cloud environments. It often falls short when sensitive data is spread across SaaS services and multiple cloud storage locations. Some teams have implemented DLP only to later discover far more exposure than expected when they added real-time data security monitoring.

DLP focuses on stopping data from leaving where it shouldn’t. But it doesn’t give enough visibility into who’s accessing what and why inside your AWS environment. It also struggles with structured databases, often requiring manual policy tuning to detect PII accurately without excessive false positives.

Macie is AWS’s built-in tool for discovering sensitive data, mostly in S3. While it’s useful, it has limitations:

• Limited scope: It primarily focuses on S3, leaving gaps in RDS, DynamoDB, and other AWS services.
• High costs: Pricing is based on the volume of data scanned, which can quickly get expensive.
• Lack of real-time monitoring: Macie provides discovery but doesn’t offer strong ongoing monitoring or access control insights.
• Limited customization: It has predefined patterns for PII detection but lacks flexibility for custom data types or business-specific rules.

DSPM tools provide better visibility than DLP or Macie, but they have their own limitations. They focus on identifying data security risks but don’t offer real-time monitoring, access controls, or automated policy enforcement. Many DSPM tools detect sensitive data but leave enforcement and remediation up to the security team, increasing manual workload.

For a deeper comparison between DSPM and other security solutions, see this guide.

A data security platform goes beyond the visibility of DSPM tools to give security teams full control over their organization’s data security. Data security platforms like Satori encompass the capabilities of DSPM, but allow engineers to enforce security policies simultaneously.

Unlike other tools, Satori:

• Provides continuous discovery of sensitive data across multiple AWS services.
• Monitors data access in real-time and enforces security policies.
• Helps manage permissions dynamically, reducing insider threats and over-permissioned users.
• Supports structured and unstructured data, scanning both databases and object storage.
• Works without requiring changes to your existing data infrastructure.

For more on Satori’s approach to data discovery, check out this article.

Satori automates data discovery and classification across AWS services, making it easy to maintain a real-time inventory of sensitive information.

Satori integrates with AWS services like S3, RDS, Redshift, and Athena. Deployment is quick and involves setting up secure connections and granting read-only permissions to analyze metadata without impacting performance. Configuration is straightforward, leveraging IAM roles and policies to ensure minimal privilege access while maintaining deep visibility.

Satori scans structured and unstructured data across AWS services. It uses machine learning-based classification, regex, and context-aware detection to accurately identify PII, financial records, and other sensitive information. Security teams can define custom classification rules to tailor discovery based on unique business needs. The Satori dashboard provides real-time insights into data locations, access patterns, and risk levels.

The alerts table provides a comprehensive view of all data store and environment alerts: security alerts, operational alerts and system alerts.

Satori continuously tracks who is accessing what data. You can set and enforce fine-grained controls, including RBAC and ABAC, ensuring least-privilege access. Just-in-time access controls dynamically grant permissions only when needed, reducing unnecessary exposure. 

Policies can be dynamically adjusted based on user roles, data sensitivity, and context, such as location or time of access. For example, an analyst might only be able to view masked versions of customer data unless explicitly approved for full access. For more information, see Satori’s access control documentation.

Satori makes it easy to generate compliance reports, showing auditors exactly where sensitive data resides and how it’s protected. Reports can be customized to align with GDPR, HIPAA, and SOC 2 requirements, reducing compliance burdens. Satori also comes with a set of out-of-the-box reports, including PII data access, sensitive data access from BI tools, and large data exports (see the full list here). 

For more details, check out Satori’s documentation.

AWS environments are complex, and sensitive data is constantly moving. Manual tracking is difficult to maintain, DLP and Macie have limitations, and DIY solutions require significant ongoing effort.

To discover and secure sensitive data in AWS, use an automated, data-centric security platform. Satori provides real-time visibility, continuous monitoring, and automated policy enforcement, giving security teams the control they need without the manual effort.

Want to see Satori in action? Learn more or schedule a demo.